In this work, an assessment of four types of risks is carried out for wind turbines during four phases, namely: transportation, installation, operation and maintenance. This work mainly focuses on onshore type of wind turbines and briefly mentioning the offshore wind turbines. The introduction gives an overview of the main parts and components of wind turbine, in addition to discussing the process of risk assessment and the procedure to be followed in this study. The paper focuses on the following four risks: the risk of transporting large-scale wind turbine parts and components, the risk of workers slipping, tripping and falling during installation and maintenance of wind turbines, the risk of working in confined spaces, and finally the risk of ice accretion and irregular shedding when the wind turbine is in operation phase or even when it is stationary. The last type of risk is highly observed in cold climate regions. The four mentioned types of risks are the main ones out of the many risks that could appear during transporting, installing, operating and maintaining wind turbines. The main aim of this work is to contribute in the proper risk assessment of potential hazards, which enhances the ability to devise passive and active protection measures to reduce the effects of a catastrophic event.
Infrastructure systems, such as wind farms, are prone to various human-induced and natural disruptions such as extreme weather conditions. There is growing concern among decision makers about the ability of wind farms to withstand and regain their performance when facing disruptions, in terms of resilience-enhanced strategies. This paper proposes a probabilistic model to calculate the resilience of wind farms facing disruptive weather conditions. In this study, the resilience of wind farms is considered to be a function of their reliability, maintainability, supportability, and organizational resilience. The relationships between these resilience variables can be structured using Bayesian network models. The use of Bayesian networks allows for analyzing different resilience scenarios. Moreover, Bayesian networks can be used to quantify resilience, which is demonstrated in this paper with a case study of a wind farm in Arctic Norway. The results of the case study show that the wind farm is highly resilient under normal operating conditions, and slightly degraded under Arctic operating conditions. Moreover, the case study introduced the calculation of wind farm resilience under Arctic black swan conditions. A black swan scenario is an unknowable unknown scenario that can affect a system with low probability and very high extreme consequences. The results of the analysis show that the resilience of the wind farm is significantly degraded when operating under Arctic black swan conditions. In addition, a backward propagation of the Bayesian network illustrates the percentage of improvement required in each resilience factor in order to attain a certain level of resilience of the wind farm under Arctic black swan conditions.
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